On May 30, 2009, at 9:36 AM, Uwe Ligges wrote:
John Poulsen wrote:
Hello,
I am using maximum likelihood to find the best parameters for a
model. This involves sometimes tweaking the starting values to
find a solution that converges.
I would like to automate the process so that when the optimizer
runs into an error it tweaks one of the parameters slightly, tries
the fit again, and then continues this until a solution if found.
I have been using try() to test if a fit will work (see below), but
how do I run a loop that says continue until class(m1) is not "try
error"?
m1<-mlefun(startvals, data=data)
if(class(m1)=="try-error"){startvals<-
list
(alpha=10,beta=1,loggamma=log(5),logk=log(exp(unlist(startvals[4]))
+0.2)) mlefun(starvals, data)}
m1 <- mlefun(starvals, data=data)
while(class(m1) == "try-error"){
startvals <- list(alpha=10, beta=1, loggamma=log(5),
logk=log(exp(unlist(startvals[4]))+0.2))
m1 <- mlefun(starvals, data=data)
}
So this implicitly assumes that try() is wrapped around the code
inside mlefun?
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
